This project aims to develop a computer-assisted consultation system for clinical radiologists in the detection of lung nodules (cancer) on chest radiographs. An automated screening of digital chest radiographs can be further developed when this system is fully tested in various clinical settings. The success of this project will inspire revolutionary improvements in other cancer diagnostic procedures through further research and development. We have demonstrated that the newly developed "vision type" neural network and training methods for the detection of lung nodules are also applicable to the detection of microcalcifications on mammograms by presenting microcalcification patterns in the training. The proposed pre-scan methods and vision type neural networks simulate radiologists' routine practices in reading chest radiographs. Radiologists' viewing patterns and decision making processes will be modeled and converted to computer readable form. Preliminary studies have shown the promise of this approach. The Phase I study will address issues related to the differentiation of end-on vessels from true nodules. The plan of the Phase H study is to (i) analyze the learning patterns of the vision type neural network, (ii) consolidate the research outcome of the neural network learning, and (iii) implement a prototype consultation system for clinical use.